Find Academic Supervisors

• Numerical Analysis
• Computational Mathematics
• Isogeometric Analysis
• +3 more

Giancarlo Sangalli is a Professor in the Department of Mathematics at the University of Pavia. His research focuses on Scientific Computing, particularly Numerical Methods and Applications, with a strong emphasis on Isogeometric Analysis (IGA) for solving Partial Differential Equations (PDEs). He leads the Scientific Computing group and contributes to interdisciplinary fields such as computational mechanics, biomedical engineering, and environmental modeling. His work integrates advanced numerical techniques, including high-order finite element methods, space-time formulations, and matrix-free solvers, to address challenges in computational efficiency and accuracy. Key areas of application include cardiac electrophysiology, wave propagation, and groundwater flow modeling. Sangalli has pioneered low-rank solvers, Tucker tensor-based methods, and immersed boundary techniques to enhance computational scalability. He actively publishes in top journals and conferences, with a focus on advancing IGA theory and its applications to real-world problems. His research also explores uncertainty quantification, Bayesian calibration, and nonlinear dynamics. Despite no explicitly listed awards, his extensive publication record reflects recognition in computational mathematics and engineering. Sangalli collaborates internationally and maintains a research website at https://mate.unipv.it/sangalli . His group's work is supported by projects in computational electromagnetics, structural mechanics, and fluid-structure interaction, demonstrating a commitment to bridging theoretical advancements with practical engineering solutions.

• Medical Imaging
• Quantitative MRI
• Magnetic Resonance Imaging (MRI) Techniques
• +5 more

Rudolf Stollberger is a Professor at TU Graz's Institute of Biomedical Imaging. His research focuses on advanced MRI techniques, including CEST-MRI, arterial spin labeling, quantitative MRI parameter quantification, and optimal control-based RF pulse design. He holds academic appointments in medical technology and has expertise in high-field MRI (7T), image reconstruction algorithms, and deep learning applications in medical imaging. Education and affiliations are not explicitly detailed, but his work spans interdisciplinary areas such as neuroimaging, perfusion quantification, and biomedical signal processing. His research portal is accessible via http://www.imt.tugraz.at/ . Key research interests include developing robust RF pulses for inhomogeneous fields, improving CEST MRI contrast via optimal control, and enhancing real-time fMRI neurofeedback systems. He has contributed to model-based reconstructions for IVIM and DTI, as well as automated segmentation methods using deep learning. Notable articles from 2025–2023 highlight innovations in MRI sequence optimization, artifact reduction, and explainable AI for medical imaging datasets. His work addresses challenges in high-resolution ASL perfusion imaging, T1 quantification, and lung vessel morphology analysis.

• Mood Disorders
• Bipolar Disorder
• Major Depressive Disorder
• +7 more

Anjali Sankar, PhD, is an Assistant Professor Adjunct of Psychiatry at Yale University's School of Medicine. Her research focuses on mood disorders, particularly bipolar disorder and major depressive disorder, with an emphasis on identifying neurobiological markers for early diagnosis and treatment optimization. She investigates brain circuitry, neuroimaging correlates of mood states, and suicide risk prediction in youth. Her work integrates functional neuroimaging (fMRI, PET), structural MRI, and psychobehavioral interventions. Education: PhD from King’s College London (2015), Postdoctoral Associate at Yale since 2018. Awards include a 2019 travel award for the Wisconsin Symposium on Emotion. Key research areas include serotonin receptor binding in depression, chronotherapeutic interventions for bipolar disorder, and brain structural correlates of psychiatric hospitalization risk. She leads studies on telehealth-based social rhythm therapy and digital tools for mood disorder management. Publications span over 20 peer-reviewed articles, with recent focus on neuroimaging biomarkers, treatment response prediction, and neurodevelopmental models of bipolar disorder. Collaborations include Hilary Blumberg, Todd Constable, and the Yale Psychiatric Institute.

Xavier TricocheView profile

Associate Professor

• Visualization and Data Analysis
• Topological Methods
• Dynamical Systems
• +3 more

Xavier Tricoche is an Associate Professor of Computer Science at Purdue University, specializing in visualization and data analysis within the Computer Graphics and Visualization Lab. His research focuses on developing algorithms and tools for exploring large, complex datasets, particularly using topological and geometric methods to analyze dynamical systems like fluid flows and planetary systems. He also works on medical imaging analysis and large-scale data representation techniques. His work spans interdisciplinary areas including computational geometry, fluid dynamics, and biomedical visualization. He actively seeks collaborations to advance these fields. Tricoche’s research emphasizes the structural analysis of data, with applications in engineering, astrophysics, and medical science. His publications highlight contributions to topological methods, flow visualization, and machine learning for scientific data.

Heiko LickertView profile

Professor

• Diabetes Research
• Beta Cell Biology
• Stem Cell Differentiation
• +3 more

Heiko Lickert is the Director of the Institute of Diabetes and Regeneration Research at Helmholtz Center Munich and W3 Professor of Beta Cell Biology in the Medical Faculty at Technical University Munich. He obtained his PhD from Albert-Ludwig University and Max-Planck Institute in Freiburg and completed postdoctoral studies at Mount Sinai Hospital, Toronto. His research focuses on: Beta cell development, regeneration and replacement therapies Pancreas organogenesis and endocrine lineage formation Stem cell-based drug screening and metabolic signaling Genetic and molecular mechanisms of diabetes His publications demonstrate sustained innovation in high-resolution imaging techniques applied to diabetes research, with recent work advancing single-cell analysis of beta cells and multimodal brain mapping. Article trends show progressive integration of transcriptomics with advanced MRI techniques. Major scientific recognitions include: ERC Advanced Grant (2022) Falling Walls Life Sciences Winner (2021) Werner-Creutzfeldt-Preis (2020) Otto-Hahn Medal (2003) He leads multiple international consortia including 'ISLET' for stem cell-based diabetes therapy and 'eISLET' for engineered pancreatic islets. As designated President of the German Stem Cell Network, he oversees national research strategy development.

• Smart Manufacturing
• Data Science
• Advanced Manufacturing Technologies
• +6 more

Ashif Iquebal is an Assistant Professor at Arizona State University's School of Computing, Informatics, and Decision Systems Engineering (CIDSE). His research focuses on smart manufacturing, data science, and advanced manufacturing technologies, particularly addressing challenges in materials-on-demand manufacturing. He holds a PhD in Industrial and Systems Engineering from Texas A&M University, along with an M.S. in Statistics and a B.Tech from IIT Kharagpur. His work emphasizes integration of IIoT, embedded sensors, and data science methods like streaming analytics and unsupervised learning. Key research interests include change point detection for streaming data, unsupervised image segmentation, and leveraging machine learning for manufacturing quality control. His contributions have been recognized through awards such as the IISE Pritsker Dissertation Award (3rd place, 2021) and multiple best student paper/poster awards at INFORMS and IISE conferences. He collaborates with institutions like Mayo Clinic on projects involving breast cancer diagnosis and segmentation using AI. Teaching responsibilities include courses on Advanced Quality Control and ASU Experience programs. His research has led to publications in top journals like IEEE Transactions on Signal Processing and Pattern Analysis and Machine Intelligence, focusing on topics spanning material characterization, process optimization, and AI-driven manufacturing solutions.

Eugene ZhangView profile

Professor

• Computer Graphics
• Scientific Visualization
• Information Visualization
• +7 more

Eugene Zhang is a Professor in the School of Electrical Engineering and Computer Science at Oregon State University. He holds a Ph.D. in Computer Science from the Georgia Institute of Technology and has been recognized with the NSF CAREER Award. His research is centered on computer graphics, scientific visualization, and geometric modeling, with applications in medical imaging, simulation, and artistic rendering. Ph.D., Computer Science, Georgia Institute of Technology, 2004 M.S., Computer Science, Ohio State University, 1995 M.S., Mathematics, Ohio State University, 1994 His research interests include computer graphics, scientific visualization, information visualization, and geometry processing. He pioneers work in vector and tensor field design, enabling more expressive and controllable visual content. His projects often involve interdisciplinary collaborations, helping scientists interpret complex data such as fluid dynamics, stress fields, and geophysical simulations. He also explores non-photorealistic rendering inspired by traditional Chinese art. The recent publications highlight a consistent trend in topological analysis of tensor and vector fields, scalable hypergraph visualization, and interactive design of complex geometric structures. His work frequently appears in IEEE VIS and SIGGRAPH venues, reflecting strong contributions to both foundational algorithms and applied visualization techniques. NSF CAREER Award (2006) Apprentice for Science and Engineering 10-Year Service Award (2018) Eugene Zhang has advised numerous Ph.D. and M.S. students, many of whom have gone on to careers in industry (e.g., Adobe, Amazon, Facebook, NVIDIA, Zoox) and academia. He is currently leading an NSF-funded project (IIS-2437424) on time-varying symmetric tensor fields. He collaborates closely with Dr. Yue Zhang on reading groups focused on graphics and simulation topics. He leads a research group focused on computer graphics and visualization, hosting regular reading sessions and mentoring graduate students in cutting-edge visualization techniques.

• Cognitive Neuroscience
• Brain Imaging (EEG, MRI)
• Brain Plasticity
• +4 more

James Clara is a Professor and Head of Research & Development at the Geneva School of Health Sciences (HES-SO), Switzerland. He holds a Privat Docent position at the University of Geneva’s Faculty of Psychology and Educational Sciences (FPSE). His research focuses on cognitive neuroscience, brain plasticity, and music’s impact on cognitive development across the lifespan, particularly in aging populations and children. He leads multiple interdisciplinary projects funded by organizations like the Swiss National Science Foundation (FNS) and Alzheimer Switzerland. Key initiatives include studies on music interventions for mild cognitive impairment (COPE project) and cognitive enrichment through arts in early childhood. Education: Doctorate in Psychology (privat docent) from University of Geneva Research interests include: - Music-induced neuroplasticity in aging brains - Brain imaging techniques (MRI/EEG) - Cognitive and sensorimotor development through music - Healthy aging strategies - Music-related musculoskeletal health in students Current projects examine: - Effects of musical training on white matter microstructure in the fornix - Cognitive benefits of group music/visual arts practice in children - Psychomotor interventions for mild cognitive impairment patients Grants: Over CHF 1M in funding for projects like COPE (2020-2022) and ArODES (2018-2021) Collaborations: Universities of Geneva/Neuchâtel, HUG hospitals, Accademia d’Archi Laboratory affiliations include the NEAD lab at University of Geneva and the HES-SO Research Institute.

Daniel Bruce EnnisView profile

Professor

• Cardiovascular MRI
• Medical Imaging
• Cardiac Mechanics
• +5 more

Professor Daniel Bruce Ennis is a leading academic in cardiovascular MRI and radiology at Stanford University. He holds a joint appointment in the Department of Radiology and a courtesy appointment in Bioengineering. As Director of Radiology Research at the Veterans Affairs Palo Alto Health Care System, he oversees a growing research program. His career includes a Ph.D. in Biomedical Engineering from Johns Hopkins, a postdoc at Stanford, and a decade leading NIH-funded studies at UCLA. He specializes in developing advanced MRI methods for assessing cardiac structure, function, and remodeling, with a focus on translational applications in adult and pediatric populations. Education: Ph.D. in Biomedical Engineering, Johns Hopkins University (2004) Postdoctoral Fellowship in Radiology and Cardiothoracic Surgery, Stanford University (2008) B.S. in Bioengineering, UC San Diego (1997) Research Interests: Dr. Ennis pioneers MRI innovations for cardiovascular diseases, including cardiac diffusion tensor imaging (cDTI), motion-compensated diffusion encoding, and strain analysis using DENSE MRI. His work emphasizes early disease detection in conditions like Duchenne muscular dystrophy and aortic coarctation, leveraging hybrid mock circulatory loops and computational models. He also develops clinical tools for non-invasive pressure estimation and valve dynamics analysis. Awards & Recognition: 2020 Distinguished Investigator, Academy for Radiology & Biomedical Imaging Research Multiple Distinguished Reviewer Awards from Magnetic Resonance in Medicine (2012-2019) Fellowships in Society for Cardiovascular Magnetic Resonance (2018) and ISMRM (2020) Grants & Leadership: Active NIH study section member (ITD, BMIT), Deputy Editor of Magnetic Resonance in Medicine , and founder of ISMRM Research Exchange Program. Current NIH grants focus on cardiovascular MRI technology development and translational applications. Labs & Teams: Leads the Radiological Sciences Lab at Stanford and collaborates with the CMR Group, Bio-X Institute, and Wu Tsai Human Performance Alliance. His team includes postdocs, graduate students, and clinical collaborators advancing MRI methodologies and cardiac device design.

Kofidis EleftheriosView profile

Associate Professor

• Signal Processing
• Machine Learning
• Digital Communications
• +3 more

Kofidis Eleftherios is an Associate Professor at the Department of Statistics and Insurance Science, University of Piraeus, Greece. He holds a Ph.D. in Computer Engineering and Informatics from the University of Patras (1996) and a Dipl. Ing. from the same institution (1990). His research focuses on signal processing, machine learning, digital communications, and biomedical applications, with a particular emphasis on tensor decompositions, filter bank multicarrier (FBMC) systems, and MIMO communication. He has contributed to over 70 peer-reviewed publications, including books and technical reports. Teaching responsibilities include courses such as Numerical Analysis, Operations Research, and Topics in Data Science. His research interests span signal processing for communication systems, tensor-based algorithms for multidimensional data analysis, and applications in biomedical signal processing. He has collaborated extensively on projects such as the European FP7-ICT EMPhAtiC initiative, focusing on MIMO channel estimation and distributed systems. Key research trends in his publications include: (1) tensor decomposition techniques for channel estimation and data fusion, (2) development of semi-blind receivers for MIMO systems, (3) analysis of filter bank-based multicarrier modulation for 5G/6G applications, and (4) biomedical signal processing using higher-order tensor methods. His recent work addresses challenges in RIS-enabled wireless systems and federated learning frameworks. Education: Ph.D., Computer Engineering and Informatics, University of Patras, 1996 Dipl. Ing., Computer Engineering and Informatics, University of Patras, 1990 Key Research Grants/Projects: European FP7-ICT Project EMPhAtiC (MIMO channel estimation and distributed systems) National and EU-funded initiatives on tensor-based signal processing Awards: None explicitly listed in the provided texts. Labs/Teams: Active in University of Piraeus's research groups on statistical signal processing and communications. His work bridges theoretical advancements in tensor methods with practical applications in wireless communications and biomedical engineering, emphasizing solutions for next-generation communication systems and multidisciplinary data fusion challenges.

Andersen AngView profile

Lecturer

• Mathematical programming/optimisation
• Nonnegative Matrix Factorisation/Tensor factorisation
• Machine learning
• +3 more

Dr Andersen Ang is a Lecturer at the University of Southampton, specializing in mathematical optimization, nonnegative matrix/tensor factorization, and machine learning applications. His research focuses on developing advanced algorithms for signal processing, convex optimization, and data analysis. He is actively supervising two PhD students in Computer Science and has published extensively in top-tier journals/conferences like IEEE Transactions on Signal Processing and SIAM Journal on Optimization. Dr Ang's work spans theoretical algorithm development (e.g., MGProx multigrid optimization, sparse NMF) and practical applications in source separation, biomedical interfaces, and educational technology. His recent publications emphasize graph signal processing, convex relaxation techniques, and scalable optimization methods for high-dimensional data. He maintains an active Google Scholar profile and a personal research website at https://angms.science/. While no specific awards were listed, his prolific publication record (over 25 articles since 2014) and focus on impactful optimization techniques demonstrate significant contributions to computational mathematics and machine learning communities. Current research includes inhomogeneous graph trend filtering and Riemannian optimization approaches for constrained problems.

Aydin BuluçView profile

Adjunct Associate Professor

• Parallel Computing
• High Performance Graph Analysis
• Machine Learning
• +4 more

Aydin Buluç is a Senior Scientist at Lawrence Berkeley National Laboratory (LBNL) and an Adjunct Associate Professor of EECS at UC Berkeley. His work focuses on parallel computing, high-performance graph analysis, machine learning, sparse computations, and computational biology. He leads the Sparsitute DOE center and directs the PASSION Lab, emphasizing scalable algorithms and their applications in genomics and data science. Education: PhD in Computer Science from UC Santa Barbara (2010); BS in Computer Science and Engineering from Sabanci University, Turkey (2005). Research: Develops parallel algorithms for graph analysis, sparse linear algebra, and communication-avoiding techniques. Key projects include HipMCL for metagenome analysis and GraphBLAS for combinatorial computing. Awards: DOE Early Career Award (2013), IEEE TCSC Early Career Award (2015), ACM Gordon Bell Prize Finalist (2022). Teaching: Co-teaches CS267 (Applications of Parallel Computers) at UC Berkeley, emphasizing practical high-performance computing. Professional Roles: Editor of ACM Transactions on Parallel Computing, PC member for major conferences (SPAA, IPDPS), and leader in ExaGraph and DOE initiatives.

• Computational Statistics
• Time Series Analysis
• Forecast Evaluation
• +3 more

Dr. Marc-Oliver Pohle is a Visiting Scientist at the Chair of Computational Statistics , part of the School of Business and Information Technology at Karlsruhe Institute of Technology (KIT). His work focuses on advanced statistical methodologies and their applications in forecasting and time series analysis. Research Interests Time Series Analysis Forecast Evaluation Multivariate Statistical Methods Long Memory Processes Statistical Calibration Recent Publications 2025: Simultaneous Inference Bands for Autocorrelations 2024: Testing Quantile Forecast Optimality 2024: Measuring Dependence between Events 2023: Generalised Covariances and Correlations 2023: Forecasting under long memory 2022: Score-based calibration testing for multivariate forecast distributions 2022: Unlucky Number 13? Manipulating Evidence Subject to Snooping 2021: Essays on Forecasting and Forecast Evaluation 2020: The Murphy decomposition and the calibration-resolution principle

Burcu TungaView profile

Associate Professor

• Image Processing
• Applied Mathematics
• Computer Science
• +12 more

Burcu Tunga is an Associate Professor in the Department of Mathematics at Istanbul Technical University, Faculty of Science and Letters. She holds a PhD in Computational Science and Engineering from the same institution and has been an active academic since 2005, progressing from Lecturer to her current rank. She has held key administrative roles, including Deputy Head of Department and membership in university-wide promotion and doctoral committees. PhD: Computational Science and Engineering, Istanbul Technical University (2004–2010) BS: Mathematics, Istanbul Technical University (1995–2000) Her research lies at the intersection of applied mathematics and computer science, with a strong emphasis on image processing, data modeling, and High Dimensional Model Representation (HDMR) . She develops and applies advanced mathematical models for image denoising, color-to-gray conversion, hyperspectral analysis, and machine learning-based diagnostics. Her work integrates fractional calculus, deep learning, and tensor decomposition to solve complex problems in medical imaging, agriculture, and materials science. Recent publications (2022–2025) show a strong trend toward hybrid models combining HDMR with deep learning and fractional operators , applied to tasks such as coffee leaf disease detection, wood defect imaging, and hyperspectral anomaly detection. These reflect a consistent focus on enhancing image quality, feature extraction, and pattern recognition through novel mathematical frameworks. Her scientific contributions are recognized through active research funding and a growing publication record. Key projects include: Anomaly Detection in Hyperspectral Images with HDMR (BAP-funded, 2024–2025) Disease Detection in Coffee Leaves with Deep Learning (completed, 2024) Content-Based Image Retrieval with HDMR (completed, 2018–2021) Burcu Tunga advises several graduate students and collaborates widely across disciplines. She leads research involving tomographic reconstruction, stress wave analysis in trees, and financial time series prediction. Her lab focuses on developing non-destructive evaluation techniques, medical CAD systems, and intelligent image processing algorithms , often leveraging HDMR as a core methodological framework.

• Neurodevelopmental Disorders
• Neonatal Brain Development
• Maternal-Fetal Psychiatry
• +3 more

Maria Lavonius is a Doctoral Researcher at the University of Turku's Department of Psychiatry. Her work focuses on neonatal brain development and maternal mental health impacts through advanced neuroimaging and electrophysiological methods. Current affiliation: Department of Psychiatry, University of Turku Academic role: Researcher Her research expertise spans multiple neurodevelopmental domains: Specializing in EEG and ERP analysis of infant auditory processing Investigating maternal-fetal interactions in psychiatric contexts Developing novel MRI segmentation techniques for neonatal brain structures Examining white matter microstructure and its developmental implications Recent publications highlight her work in: 2025: Quantitative EEG parameter analysis across gestational and postnatal timelines 2023: Nordic cancer treatment protocol standardization (as secondary author) 2022-2018: Auditory mismatch responses, maternal sleep impacts, and DTI reliability metrics